The present invention relates to systems for diagnosing the state of operation of an apparatus, equipment, facilities or the like (hereinafter collectively called "equipment") on the basis of information of vibrations of the equipment, and specifically to state diagnostic systems for equipment, in which the state of operation of the equipment is diagnosed by using results of learning of information on vibrations in the past.
In general, an abnormality which has occurred in equipment is often detected as specific vibrations, for example, as abnormal mechanical vibrations. In the case of rotating machines for example, such abnormalities often appear as vibrations of their shafts. It has hence been attempted to determine the presence or absence abnormalities in equipment and further to estimate their causes by monitoring vibrations such as vibrations of shafts.
As a first conventional method, there is the method discussed in An ASME Publication, 81-JPGC-Pwr-28. According to this method, the waveform of vibrations recorded is subjected to a spectrum analysis and the cause of the abnormality is estimated using a diagnostic logic table.
A second conventional method is discussed in the paper (entitled: The Development and Application of TURBOMAC An Expert Machinery Diagnostic System) added at the EPRI Seminar on Expert Systems Applications in Power Plants, May 27-29, 1989, Boston. This method is applied to estimate the cause of each abnormality by using knowledge engineering. According to this method, the cause of the abnormality is determined by performing a search among a number of diagnostic rules provided in advance.
The above conventional techniques are however accompanied by the following problems.
In the first method, spectrum analyses must be performed in real time. It is thus indispensable to use a special processor or high-performance computer for this purpose, whereby the diagnostic system becomes costly. Further, a diagnostic logic table is prepared by an expert or specialist well versed in abnormal phenomena of the target equipment such as a rotating machine. A great deal of time is therefore required for the preparation of such a diagnostic logic table. In addition, diagnostic results reflect individual differences of the person who prepared the logic, so that they lack objectivity.
Further, neither the degree of each abnormality (hereinafter called the "abnormality level") nor the reliability of the diagnostic results (hereinafter called the "certainty factor") is shown by the diagnostic logic table, whereby a user (e.g., an operator) does not know how much he should rely upon the importance and reliability of the diagnostic results. Accordingly, the evaluation of the diagnostic results is also highly dependent on the subjectivity of the user. Use of such a diagnostic logic table therefore involves problems in objectivity.
In the second method on the other hand, the group of diagnostic rules is constructed in the form of a large relational tree. A lot of time is therefore required for inference (the search of the relational tree), so that the second method is practiced as an off-line diagnosis. To perform the second method in real time, a high-performance computer is required. Further, as in the first conventional method, the diagnostic rules are prepared by an expert or specialist well versed in abnormal phenomena of the target equipment. A lot of time is therefore required for their preparation. In addition, diagnostic results reflect individual differences of the person who prepared the diagnostic rules, and lack objectivity. Further, base data for diagnoses are selected by a user from multiple-choice data on the signs of abnormalities and are inputted in a diagnostic system. Therefore, diagnostic results inevitably reflect individual differences of the user. As a result, the certainty factor also reflect the arbitrariness of both the person who prepared the diagnostic rules and the person who inputted base data for diagnoses. The certainty factor therefore does not have persuasive power.
As has been described above, the above conventional techniques are accompanied by the drawbacks that they require a lot of time for the preparation of a diagnostic logic, they cannot provide any processing speed fast enough to perform real time processing, and diagnostic results lack objectivity.